Monitoring processes of runoff and sediment are the foundation of the dynamic assessment of soil erosion. Runoff
volume and sediment concentration are 2 important hydrodynamic parameters to forecast slope runoff variation, reveal soil
erosion mechanism and find out movement rule of soil in the field. However, the equipment which can be used widely to
monitor precisely the runoff volume and sediment concentration at the same time is lacking. In view of the inaccuracy or the
lack of equipment for automatic monitoring of runoff and sediment processes, real-time and automatic measuring equipment
with high precision was explored in this study. The equipment could be applied to multi-scenario, including runoff plot and
watershed. In our study, firstly, through the integration of signal sensing and automatic control technology, physical
characteristics driving the operation of the equipment were extracted and converted into TT&C signals to automatically
monitor runoff and sediment yield processes. On the one hand, continuous flow of runoff and sediment was discretized by
optimizing the design of equipment overall structure and the functional unit. On the other hand, optical material of sampling
device was used and the most reasonable shape and surface treatment of sampling device was designed, to reduce the
influences of sediment adhesion and deposition on the accuracy and precision of measurement. Finally, based on “internet+”
framework, a cloud station/data management platform was established, including station management, data integration,
calculation and analysis, and application service. The equipment was able to get the values of runoff volume and sediment
concentration synchronously, which overcame the limitation of tradition method. Furthermore, the reliability and applicability
of the equipment were validated by the simulation experiment, and the results showed that the relative error of sediment
concentration was averaged as 3.67%. Specifically, the relative error was averaged as 7.00%, when the sediment concentration
was less than10 kg/m3, and when the sediment concentration varied from 20 to 90 kg/m3 and from 100 to 300 kg/m3, the
averaged relative errors were 3.10% and 2.61% respectively. The relative error greater than 10% accounted for only 3.70% of
the total samples, while the relative error of the remaining 96.3% was less than 10%. The results also showed that the slope of
linear regression between measured and actual sediment concentration was close to 1, and the coefficient of determination was
up to 0.997. The research demonstrates that the equipment can detect precisely the dynamic processes of runoff volume and
sediment concentration. We also monitored the dynamic process of runoff volume and sediment concentration through soil bin
simulation experiment, and found that runoff volume varied from 19 to 127 L/s, with an average of 75.5 L/s, sediment
concentration ranged from 4.6 to 275.1 kg/m3, and sediment concentration in the single rainfall was, on average, 88.6 kg/m3.
The finding demonstrates that the equipment is capable of monitoring the large variation of runoff and sediment concentration,
and can be used to complex field observations, and therefore, the self-designed equipment for auto-sampling water from runoff
has a good prospect. This research can provide new techniques and methods for water and soil loss study, and promote the
automation and informatization in water and soil loss monitoring.